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Let’s Wangle With Web Scraping!

Introduction

Off late, usage of websites has tremendously increased and so has the need for sophisticated activities like Web Scraping or Data Extraction from those websites. But, extracting data manually from web pages can be a tiresome and redundant process. The need for using highly advanced tools, techniques and methods are in demand to get the data in desired formats. This is where Web Scraping comes to the rescue.

In simple words, it is the process of automating the extraction of data (in any format) from websites. It is a basic practice to gather the data that you need by primarily using a computer program or by using various tools to parse the raw and rich data.

Let us talk about ‘Python’ due to its diverse and rich set of libraries, plugins and open-source code availability.

Pick the precise tool

Selecting the most effective tool depends on the nature of the project you are working on since Python has a wide variety of sources, libraries and frameworks for web scraping. It’s our job to choose the most appropriate tool for our project. We need to understand the pros and cons of the tool that might aid in deciding the best fit for our project, which will benefit in practical devising that may preserve the man-hours upfront.

The three most popular tools for web scraping are:

  • BeautifulSoup: It’s a library for parsing HTML and XML documents. “Requests” (controls HTTP assemblies and performs HTTP requests) in sequence with “BeautifulSoup” (a parsing library) are the most suited kits for meager and agile web scraping. This is the appropriate tool for scraping more simplistic, immobile and less-JS associated complexities. “lxml” is a high-performance, simple, secure and more diverse parsing library which is an eminent alternative to BeautifulSoup.
  • Scrapy: It is a web creeping framework that gives a flawless tool for scraping. In this, we build Spiders which are python properties that determine how a selective site(s) will be scraped. Scrapy makes an exceptional choice to make a robust, collective, scalable, high scale scraper. It also comes with a collection of middleware for redirects, sessions, cookies, caching etc. which assists us to deal with various complexities that we might come across.
  • Selenium: For huge JS contributed pages or highly complicated websites, Selenium WebDriver is the most helpful tool to choose from. It has a mechanism that automates the web browsers, also kenned as a WebDriver. With this, one can initiate an Internet Explorer/ Microsoft Edge/ Google Chrome/ Mozilla Firefox or any other browser to automate the window, which opens a URL and surfs on the links. But, it’s not as effective as the mechanisms which we have examined to date. This tool is preferable to work with when all gateways of web scraping are being stopped and we still require data which is of significance.

Powerful Pages – Client-side Web pages Rendering

Although websites are frequently becoming more interactive, immersive and user-friendly, this has an impact on web crawlers. Most of the modern websites use several powerful coding methods that are not at all crawler friendly. Some of the samples are lazy-image-loading, infinite-scrolling, or components loaded via AJAX calls, which present difficulty in crawling. Most of the developed websites massively rely on JavaScript to load dynamic components. One can detect whether a web page operates by asynchronous stacking or if it is an active live page by checking the source of the page (“View page source” option is shown when we right-click on the page).

For Selenium, we need to download the Chrome web driver/ Firefox web driver and set it in the location of our Python script. Also, we need to install the Selenium Python package, pip install – selenium if not installed.

Sometimes, fetching content from dynamic sites is easy, because they are extremely reliant on API calls. In asynchronous loading, data is loaded by executing GET and POST requests; one can observe these API calls in the “Network” tab of Developer Tools. It is always useful to inspect the Network’s tab before going to the Selenium WebDriver option because requesting an API is extremely fast when compared with managing it with a web driver.

Authentication

Sometimes we have to scrape private data, which is accessible once it gets verified on the website. The simplest way to control authentication is by using a web driver. We can automate with a web driver using the Selenium library in python, which can be achieved like a mascot!

Automated Turing Tests

This is a type of test-response examination employed in computing to decide whether or not the user is human. To get freed of the captchas, we need middleware that can solve the captchas.

Web services like Cloudflare thwart bots and present DDoS security assistance make it even harder for bots to accomplish their jobs. Crawlera is an interesting choice to manipulate redirect and captchas. Mild text-based captchas can be resolved by utilizing Optical Character Recognition (OCR). We can apply “pytesseract”, a python library for decoding captchas.

Unraveling captchas is a significant burden in the scraping process, one needs to get relieved of this overhead by taking the aid of APIs such as “Anti Captcha” and “Death by Captcha”.

IP Blocking

IP address blocking is another concern that a web crawler encounters. If we are performing the request more frequently to the website with the same IP, then there is a greater chance that the site will hinder our IP address. Some websites use anti-scraping technologies which make it difficult to scrape. It is perpetually more helpful to switch IPs and utilize proxy services and VPN settings so that our spider won’t get obstructed. It will accommodate to depreciate the uncertainty of getting blackballed.

There are a bunch of APIs available to manipulate IP blocking, such as “scraperapi” which can be simply blended within the scraping project.

Go the EXTRA mile – The Node.js way!

We can also apply web scraping methods (particularly by using DOM parsing) to derive data from a website. We can use the “Cheerio” package to parse the content of a website employing accessible DOM techniques. We can achieve more superior parsing using decapitated browsers like JSDOM and PhantomJS. Other packages like Osmosis, Axios, Puppeteer, WGET, webdriver.io and casperjs are also a few advanced techniques that are efficient for data scraping from the web. These dependencies can be installed using a Node Package Manager (NPM) command i.e., npm install puppeteer.

It can also be used to take screenshots and create PDFs of the respective pages. This can be further utilized in such a way that the testing environment is automated and maintained up-to-date.

Developer Essentials:

  • Download pip fromhttps://bootstrap.pypa.io/get-pip.py and run ‘get-pip.py’ by navigating to the folder.
  • Similarly install other required packages through pip, “pip install” is common for any package. Install and specify the package name to it (Example: “pip install beautifulsoup4”, where “beautifulsoup4” is the name of the package).
  • Package examples: mechanize, pandas, re, time, geopy, json, requests, ssl, urllib2, xlwt, urllib3, reverse_geocoder and scraper.

Conclusion

This article helps in learning and understanding the complex pitfalls and roadblocks that we may encounter during web scraping, comprehending smart and practical ideas to work through and get web scraping in-depth. Web scraping is a huge area, and we have just ended a short journey of it.

Contact for further details

Vasu Singampalli
Team Lead – Digital Java
vasus.in@mouritech.com
MOURI Tech

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